docs: def ensure_type(val, dtype, ndim, name, length=None, can_be_none=False, shape=None,
                  warn_on_cast=True, add_newaxis_on_deficient_ndim=False):
      """Typecheck the size, shape and dtype of a numpy array, with optional
      casting.
  
      Parameters
      ----------
      val : {np.ndaraay, None}
          The array to check
      dtype : {nd.dtype, str}
          The dtype you'd like the array to have
      ndim : int
          The number of dimensions you'd like the array to have
      name : str
          name of the array. This is used when throwing exceptions, so that
          we can describe to the user which array is messed up.
      length : int, optional
          How long should the array be?
      can_be_none : bool
          Is ``val == None`` acceptable?
      shape : tuple, optional
          What should be shape of the array be? If the provided tuple has
          Nones in it, those will be semantically interpreted as matching
          any length in that dimension. So, for example, using the shape
          spec ``(None, None, 3)`` will ensure that the last dimension is of
          length three without constraining the first two dimensions
      warn_on_cast : bool, default=True
          Raise a warning when the dtypes don't match and a cast is done.
      add_newaxis_on_deficient_ndim : bool, default=True
          Add a new axis to the beginining of the array if the number of
          dimensions is deficient by one compared to your specification. For
          instance, if you're trying to get out an array of ``ndim == 3``,
          but the user provides an array of ``shape == (10, 10)``, a new axis will
          be created with length 1 in front, so that the return value is of
          shape ``(1, 10, 10)``.
  
      Notes
      -----
      The returned value will always be C-contiguous.
  
      Returns
      -------
      typechecked_val : np.ndarray, None
          If `val=None` and `can_be_none=True`, then this will return None.
          Otherwise, it will return val (or a copy of val). If the dtype wasn't right,
          it'll be casted to the right shape. If the array was not C-contiguous, it'll
          be copied as well.
  
      """
      if can_be_none and val is None:
          return None
  
      if not isinstance(val, np.ndarray):
          # special case: if the user is looking for a 1d array, and
          # they request newaxis upconversion, and provided a scalar
          # then we should reshape the scalar to be a 1d length-1 array
          if add_newaxis_on_deficient_ndim and ndim == 1 and np.isscalar(val):
              val = np.array([val])
          else:
              raise TypeError(("%s must be numpy array. "
                               " You supplied type %s" % (name, type(val))))
  
      if warn_on_cast and val.dtype != dtype:
          warnings.warn("Casting %s dtype=%s to %s " % (name, val.dtype, dtype),
                        TypeCastPerformanceWarning)
  
      if not val.ndim == ndim:
          if add_newaxis_on_deficient_ndim and val.ndim + 1 == ndim:
              val = val[np.newaxis, ...]
          else:
              raise ValueError(("%s must be ndim %s. "
                                "You supplied %s" % (name, ndim, val.ndim)))
  
      val = np.ascontiguousarray(val, dtype=dtype)
  
      if length is not None and len(val) != length:
          raise ValueError(("%s must be length %s. "
                            "You supplied %s" % (name, length, len(val))))
  
      if shape is not None:
          # the shape specified given by the user can look like (None, None 3)
          # which indicates that ANY length is accepted in dimension 0 or
          # dimension 1
          sentenel = object()
          error = ValueError(("%s must be shape %s. You supplied  "
                              "%s" % (name, str(shape).replace('None', 'Any'), val.shape)))
          for a, b in zip_longest(val.shape, shape, fillvalue=sentenel):
              if a is sentenel or b is sentenel:
                  # if the sentenel was reached, it means that the ndim didn't
                  # match or something. this really shouldn't happen
                  raise error
              if b is None:
                  # if the user's shape spec has a None in it, it matches anything
                  continue
              if a != b:
                  # check for equality
                  raise error
  
      return val